Battle of Neighborhoods in New York City: Opening a Pizza Place

Introduction¶

New York City is the most populated city in the United States. It is a diverse city that attracts lots of Bussinesses each year. One of the most popular bussinesses in NYC is restaurant. There are enough restaurants in NYC that you can eat for 23 years without eating in a restaurant twice. The large number of restaurants doesn't mean that all of them are successful. To thrive in such an environment, you need to do intensive study before openning one. Let's assume that we want to add a Italian Restaurant to the pile of restaurants in NYC. We would like to know where is the best place to open it.

Table of Contents¶

1. Download and Explore Dataset
2. Explore Neighborhoods in New York City
3. Analyze Each Neighborhood
4. Cluster Neighborhoods
5. Examine Clusters

Before we get the data and start exploring it, let's download all the dependencies that we will need.

import numpy as np # library to handle data in a vectorized manner

import pandas as pd # library for data analsysis
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)

import json # library to handle JSON files

# !conda install -c conda-forge geopy --yes
from geopy.geocoders import Nominatim # convert an address into latitude and longitude values

import requests # library to handle requests
from pandas import json_normalize # tranform JSON file into a pandas dataframe

# Matplotlib and associated plotting modules
import matplotlib.cm as cm
import matplotlib.colors as colors
import matplotlib.pyplot as plt

# import k-means from clustering stage
from sklearn.cluster import KMeans

# !conda install -c conda-forge folium=0.5.0 --yes
import folium # map rendering library

print('Libraries imported.')

Libraries imported.

1. Download and Explore Dataset¶

New York has a total of 5 boroughs and 306 neighborhoods. We need to have a dataset that includes the essential information about each of these neighborhoods. Part of the data used in this project is extracted from the Foursquare as we go forward. So, the only information that we need from each neighborhood is it's location.

Luckily, this dataset exists for free on the web. Here is the link to the dataset: https://geo.nyu.edu/catalog/nyu_2451_34572 Due to being one of the largest cities in US, there are lots of more data available for this city that can be found in internet.

Load and explore the data¶

For your convenience, I downloaded the files and placed it on the repository. Let's load it.

with open('newyork_data.json') as json_data:
    newyork_data = json.load(json_data)

Let's take a quick look at the data.

type(newyork_data)

dict

newyork_data.keys()

dict_keys(['type', 'totalFeatures', 'features', 'crs', 'bbox'])

Notice how all the relevant data is in the features key, which is basically a list of the neighborhoods. So, let's define a new variable that includes this data.

neighborhoods_data = newyork_data['features']

Let's take a look at the first item in this list.

neighborhoods_data[0]

{'type': 'Feature',
 'id': 'nyu_2451_34572.1',
 'geometry': {'type': 'Point',
  'coordinates': [-73.84720052054902, 40.89470517661]},
 'geometry_name': 'geom',
 'properties': {'name': 'Wakefield',
  'stacked': 1,
  'annoline1': 'Wakefield',
  'annoline2': None,
  'annoline3': None,
  'annoangle': 0.0,
  'borough': 'Bronx',
  'bbox': [-73.84720052054902,
   40.89470517661,
   -73.84720052054902,
   40.89470517661]}}

As we can see, for each neighborhood, this dataset provides its name, borough, and location. We need to extract this information and convert them to a form that can be used in Python.

Tranform the data into a pandas dataframe¶

# define the dataframe columns
column_names = ['Borough', 'Neighborhood', 'Latitude', 'Longitude'] 

# instantiate the dataframe
NYC_neighborhoods = pd.DataFrame(columns=column_names)
for data in neighborhoods_data:
    borough = neighborhood_name = data['properties']['borough'] 
    neighborhood_name = data['properties']['name']
        
    neighborhood_latlon = data['geometry']['coordinates']
    neighborhood_lat = neighborhood_latlon[1]
    neighborhood_lon = neighborhood_latlon[0]
    
    NYC_neighborhoods = NYC_neighborhoods.append({'Borough': borough,
                                          'Neighborhood': neighborhood_name,
                                          'Latitude': neighborhood_lat,
                                          'Longitude': neighborhood_lon}, ignore_index=True)

Let's look at the dataframe to confirm that it's correct.

NYC_neighborhoods.head()

And make sure that the dataset has all 5 boroughs and 306 neighborhoods.

print('The dataframe has {} boroughs and {} neighborhoods.'.format(
        len(NYC_neighborhoods['Borough'].unique()),
        NYC_neighborhoods.shape[0]
    )
)

The dataframe has 5 boroughs and 306 neighborhoods.

Use geopy library to get the latitude and longitude values of New York City.¶

We will use this information to show the map of the new york city.

address = 'New York City, NY'

geolocator = Nominatim(user_agent="ny_explorer")
location = geolocator.geocode(address)
latitude = location.latitude
longitude = location.longitude
print('The geograpical coordinate of New York City are {}, {}.'.format(latitude, longitude))

The geograpical coordinate of New York City are 40.7127281, -74.0060152.

Create a map of New York with neighborhoods superimposed on top.¶

To get a better understanding of the data, let's show an interactive map of the city with neighborhoods.

# create map of New York using latitude and longitude values
map_newyork = folium.Map(location=[latitude, longitude], zoom_start=10)

# add markers to map
for lat, lng, borough, neighborhood in zip(NYC_neighborhoods['Latitude'], NYC_neighborhoods['Longitude'], NYC_neighborhoods['Borough'], NYC_neighborhoods['Neighborhood']):
    label = '{}, {}'.format(neighborhood, borough)
    label = folium.Popup(label, parse_html=True)
    folium.CircleMarker(
        [lat, lng],
        radius=4,
        weight=1,
        popup=label,
        color='white',
        fill=True,
        fill_color='#3186cc',
        fill_opacity=0.7,
        parse_html=False).add_to(map_newyork)  
    
map_newyork

Next, we are going to start utilizing the Foursquare API to explore the neighborhoods and segment them.

Define Foursquare Credentials and Version¶

import os
from dotenv import load_dotenv
load_dotenv()

CLIENT_ID =  os.getenv('CLIENT_ID')
CLIENT_SECRET = os.getenv('CLIENT_SECRET')
VERSION=20200222

Let's explore the first neighborhood in our dataframe.¶

Let's show the workflow for a neighborhood.

print('The neighborhood is', NYC_neighborhoods.loc[0, 'Neighborhood'], 'in', NYC_neighborhoods.loc[0, 'Borough'],'.')
neighborhood_latitude = NYC_neighborhoods.loc[0, 'Latitude'] # neighborhood latitude value
neighborhood_longitude = NYC_neighborhoods.loc[0, 'Longitude'] # neighborhood longitude value

neighborhood_name = NYC_neighborhoods.loc[0, 'Neighborhood'] # neighborhood name

print('It\'s latitude and longitude values are {}, {}.'.format(neighborhood_latitude, 
                                                               neighborhood_longitude))

The neighborhood is Wakefield in Bronx .
It's latitude and longitude values are 40.89470517661, -73.84720052054902.

Now, let's get the top 200 venues that are in Wakefield within a radius of 500 meters.¶

# type your answer here
search_query=''
radius= 500
LIMIT= 200
url = 'https://api.foursquare.com/v2/venues/explore?&client_id={}&client_secret={}&v={}&ll={},{}&radius={}&limit={}'.format(
    CLIENT_ID, 
    CLIENT_SECRET, 
    VERSION, 
    neighborhood_latitude, 
    neighborhood_longitude, 
    radius, 
    LIMIT)
results = requests.get(url).json()

# function that extracts the category of the venue
def get_category_type(row):
    try:
        categories_list = row['categories']
    except:
        categories_list = row['venue.categories']
        
    if len(categories_list) == 0:
        return None
    else:
        return categories_list[0]['name']

venues = results['response']['groups'][0]['items']
    
nearby_venues = json_normalize(venues) # flatten JSON

# filter columns
filtered_columns = ['venue.name', 'venue.categories', 'venue.id', 'venue.location.lat', 'venue.location.lng']
nearby_venues =nearby_venues.loc[:, filtered_columns]

# filter the category for each row
nearby_venues['venue.categories'] = nearby_venues.apply(get_category_type, axis=1)

# clean columns
nearby_venues.columns = [col.split(".")[-1] for col in nearby_venues.columns]

print('{} venues were returned by Foursquare.'.format(nearby_venues.shape[0]))
nearby_venues

11 venues were returned by Foursquare.

2. Explore Neighborhoods in New York City¶

Let's create a function to repeat the same process to all the neighborhoods¶

def getNearbyVenues(names, latitudes, longitudes, radius=500):
    
    venues_list=[]
    for name, lat, lng in zip(names, latitudes, longitudes):
#         print(name)
            
        # create the API request URL
        url = 'https://api.foursquare.com/v2/venues/explore?&client_id={}&client_secret={}&v={}&ll={},{}&radius={}&limit={}'.format(
            CLIENT_ID, 
            CLIENT_SECRET, 
            VERSION, 
            lat, 
            lng, 
            radius, 
            LIMIT)
            
        # make the GET request
        results = requests.get(url).json()["response"]['groups'][0]['items']
        
        # return only relevant information for each nearby venue
        venues_list.append([(
            name, 
            lat, 
            lng, 
            v['venue']['name'],
            v['venue']['id'],
            v['venue']['location']['lat'], 
            v['venue']['location']['lng'],  
            v['venue']['categories'][0]['name']) for v in results])

    nearby_venues = pd.DataFrame([item for venue_list in venues_list for item in venue_list])
    nearby_venues.columns = ['Neighborhood', 
                  'Neighborhood Latitude', 
                  'Neighborhood Longitude', 
                  'Venue',
                  'Venue id',
                  'Venue Latitude', 
                  'Venue Longitude', 
                  'Venue Category']
    
    return(nearby_venues)

NY_venues = getNearbyVenues(names=NYC_neighborhoods['Neighborhood'],
                                   latitudes=NYC_neighborhoods['Latitude'],
                                   longitudes=NYC_neighborhoods['Longitude']
                                  )

print('done!')

done!

Let's check the size of the resulting dataframe¶

print('In total',  NY_venues.shape[0], 'venues were found in New York')
NY_venues.head()

In total 10249 venues were found in New York

Let's check how many venues were returned for each neighborhood

Venue_count= NY_venues.groupby('Neighborhood').count()[['Venue']].sort_values('Venue', ascending=False)
Venue_count.reset_index(inplace=True)
Venue_count.head(10)

As we can see, the most venues were found in Murray Hill. Let's show the density of the venues in each neighborhood.

NY_geo = r'NTA.geojson' # geojson file

# create a plain world map
NY_map = folium.Map(location=[latitude, longitude], zoom_start=10)
# folium.Map(location=[latitude,longitude], zoom_start=11, tiles='Mapbox Bright')

# generate choropleth map using the total immigration of each country to Canada from 1980 to 2013
NY_map.choropleth(
    geo_data=NY_geo,
    data=Venue_count,
    columns=['Neighborhood', 'Venue'],
    key_on='feature.properties.ntaname',
    fill_color='YlOrRd', 
    fill_opacity=0.7, 
    line_opacity=0.2,
    legend_name='Number of Venues'
)

# display map
NY_map

Let's find out how many unique categories can be curated from all the returned venues¶

print('There are {} uniques categories.'.format(len(NY_venues['Venue Category'].unique())))

There are 433 uniques categories.

3. Analyze Each Neighborhood¶

Let's prepare the data for clustering. To do so, we would convert categories to columns where 1 means that category is in that neighborhood.

# one hot encoding
NY_onehot = pd.get_dummies(NY_venues[['Venue Category']], prefix="", prefix_sep="")

# add neighborhood column back to dataframe
NY_onehot['Neighborhood'] = NY_venues['Neighborhood'] 

# move neighborhood column to the first column
cols = list(NY_onehot)
cols.insert(0, cols.pop(cols.index('Neighborhood')))
NY_onehot = NY_onehot.loc[:, cols]

NY_onehot.head()

And let's examine the new dataframe size.

NY_onehot.shape

(10249, 433)

Next, let's group rows by neighborhood and by taking the mean of the frequency of occurrence of each category¶

NY_grouped = NY_onehot.groupby('Neighborhood').mean().reset_index()
NY_grouped.head()

sorted_venue= pd.DataFrame(NY_grouped.mean().sort_values(ascending=False)[0:15], columns=['frequency'])
ax=sorted_venue.plot(kind='barh',rot=15,figsize=(6,6))
ax.invert_yaxis()
a = ax.barh(3, sorted_venue.iloc[3], height=0.5, color = 'red')
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.spines['left'].set_visible(False)
plt.legend([])
plt.xlabel('Frequency')
plt.title('Most Popular Venues in New York')
plt.show()

As we can see, Italian restaurant is the $4^{th}$ most popular venue in New York City.

Let's confirm the new size¶

NY_grouped.shape

(301, 433)

Let's put that into a pandas dataframe¶

First, let's write a function to sort the venues in descending order.

def return_most_common_venues(row, num_top_venues):
    row_categories = row.iloc[1:]
    row_categories_sorted = row_categories.sort_values(ascending=False)
    
    return row_categories_sorted.index.values[0:num_top_venues]

Now let's create the new dataframe and display the top 10 venues for each neighborhood.

num_top_venues = 10

indicators = ['st', 'nd', 'rd']

# create columns according to number of top venues
columns = ['Neighborhood']
for ind in np.arange(num_top_venues):
    try:
        columns.append('{}{} Most Common Venue'.format(ind+1, indicators[ind]))
    except:
        columns.append('{}th Most Common Venue'.format(ind+1))

# create a new dataframe
neighborhoods_venues_sorted = pd.DataFrame(columns=columns)
neighborhoods_venues_sorted['Neighborhood'] = NY_grouped['Neighborhood']

for ind in np.arange(NY_grouped.shape[0]):
    neighborhoods_venues_sorted.iloc[ind, 1:] = return_most_common_venues(NY_grouped.iloc[ind, :], num_top_venues)

neighborhoods_venues_sorted.head()

4. Cluster Neighborhoods¶

Run k-means to cluster the neighborhood into 5 clusters.

# set number of clusters
kclusters = 7

NY_grouped_clustering = NY_grouped.drop('Neighborhood', 1)

# run k-means clustering
kmeans = KMeans(n_clusters=kclusters, random_state=0).fit(NY_grouped_clustering)

# check cluster labels generated for each row in the dataframe
kmeans.labels_[:]

array([0, 0, 0, 6, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 3, 0, 1, 0, 1, 5, 0,
       1, 0, 1, 1, 1, 1, 0, 5, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 5, 0, 1,
       1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 3, 0, 1, 1, 0, 1, 0, 1, 0, 0, 5,
       0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1,
       1, 1, 0, 4, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 6, 0, 1, 0,
       1, 0, 0, 1, 1, 6, 1, 1, 0, 0, 1, 0, 1, 6, 1, 5, 0, 0, 0, 1, 1, 0,
       0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 5, 1, 1, 1, 1, 0,
       1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 6, 0, 0, 1, 5, 1, 1, 0,
       0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 5, 6, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0,
       1, 1, 1, 1, 0, 0, 1, 6, 1, 0, 1, 0, 0, 0, 0, 2, 1, 0, 0, 1, 1, 0,
       1, 0, 1, 6, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 5, 0, 1, 1, 0, 0, 5, 0,
       1, 0, 5, 1, 1, 1, 1, 3, 0, 5, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0,
       1, 0, 1, 1, 3, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1,
       1, 1, 0, 1, 1, 5, 1, 6, 1, 0, 0, 0, 0, 1, 1])

Let's create a new dataframe that includes the cluster as well as the top 10 venues for each neighborhood.

# add clustering labels
try:
    neighborhoods_venues_sorted.insert(0, 'Cluster Labels', kmeans.labels_)
except:
    neighborhoods_venues_sorted['Cluster Labels']=kmeans.labels_

NY_merged = NYC_neighborhoods

# merge toronto_grouped with toronto_data to add latitude/longitude for each neighborhood
NY_merged = NY_merged.join(neighborhoods_venues_sorted.set_index('Neighborhood'), on='Neighborhood')
NY_merged=NY_merged.dropna()
NY_merged['Cluster Labels']= NY_merged['Cluster Labels'].astype('int')
NY_merged.head() # check the last columns!

Finally, let's visualize the resulting clusters

# create map
map_clusters = folium.Map(location=[latitude, longitude], zoom_start=10)

# set color scheme for the clusters
x = np.arange(kclusters)
ys = [i + x + (i*x)**2 for i in range(kclusters)]
colors_array = cm.rainbow(np.linspace(0, 1, len(ys)))
rainbow = [colors.rgb2hex(i) for i in colors_array]

# add markers to the map
markers_colors = []
for lat, lon, poi, cluster in zip(NY_merged['Latitude'], NY_merged['Longitude'], NY_merged['Neighborhood'], NY_merged['Cluster Labels']):
    label = folium.Popup(str(poi) + ' Cluster ' + str(cluster), parse_html=True)
    folium.CircleMarker(
        [lat, lon],
        radius=3,
        popup=label,
        color=rainbow[cluster-1],
        weight=1,
        fill=True,
        fill_color=rainbow[cluster-1],
        fill_opacity=0.7).add_to(map_clusters)
       
map_clusters

NY_merged.head()

5. Examine Clusters¶

Now, you can examine each cluster and determine the discriminating venue categories that distinguish each cluster.

Cluster 1¶

CL1=pd.DataFrame(NY_merged[NY_merged['Cluster Labels']==0].groupby(['1st Most Common Venue']).count().sort_values('Neighborhood',ascending=False).iloc[:5,0])
CL1.rename(columns={'Borough':'Count'})

This cluster includes the most popular venue in New York; Pizza Place.

Cluster 2¶

CL2=pd.DataFrame(NY_merged[NY_merged['Cluster Labels']==1].groupby(['1st Most Common Venue']).count().sort_values('Neighborhood',ascending=False).iloc[:5,0])
CL2.rename(columns={'Borough':'Count'})

This is the cluster that we are interested in. The most popular venue in this cluster is the Italian Restaurant. Let's show this cluster on the map.

NY_map

# set color scheme for the clusters
x = np.arange(kclusters)
ys = [i + x + (i*x)**2 for i in range(kclusters)]
colors_array = cm.rainbow(np.linspace(0, 1, len(ys)))
rainbow = [colors.rgb2hex(i) for i in colors_array]

# add markers to the map
markers_colors = []
for lat, lon, poi, cluster in zip(NY_merged[NY_merged['Cluster Labels']==1]['Latitude'], NY_merged[NY_merged['Cluster Labels']==1]['Longitude'], NY_merged[NY_merged['Cluster Labels']==1]['Neighborhood'], NY_merged[NY_merged['Cluster Labels']==1]['Cluster Labels']):
    label = folium.Popup(str(poi) + ' Cluster ' + str(cluster), parse_html=True)
    folium.CircleMarker(
        [lat, lon],
        radius=3,
        popup=label,
        color=rainbow[cluster-1],
        weight=1,
        fill=True,
        fill_color=rainbow[cluster-1],
        fill_opacity=0.7).add_to(NY_map)
       
NY_map

The map shows the neighborhoods that are in this cluster on top of the venue density in New York. The best place to open a new italian restaurant is in these neighborhoods with denser venue numbers as more people go to these places.

Cluster 3¶

CL3=pd.DataFrame(NY_merged[NY_merged['Cluster Labels']==2].groupby(['1st Most Common Venue']).count().sort_values('Neighborhood',ascending=False).iloc[:5,0])
CL3.rename(columns={'Borough':'Count'})

Cluster 4¶

CL4=pd.DataFrame(NY_merged[NY_merged['Cluster Labels']==3].groupby(['1st Most Common Venue']).count().sort_values('Neighborhood',ascending=False).iloc[:5,0])
CL4.rename(columns={'Borough':'Count'})

Cluster 5¶

CL5=pd.DataFrame(NY_merged[NY_merged['Cluster Labels']==4].groupby(['1st Most Common Venue']).count().sort_values('Neighborhood',ascending=False).iloc[:5,0])
CL5.rename(columns={'Borough':'Count'})

Cluster 6¶

CL6=pd.DataFrame(NY_merged[NY_merged['Cluster Labels']==5].groupby(['1st Most Common Venue']).count().sort_values('Neighborhood',ascending=False).iloc[:5,0])
CL6.rename(columns={'Borough':'Count'})

Cluster 7¶

CL7=pd.DataFrame(NY_merged[NY_merged['Cluster Labels']==6].groupby(['1st Most Common Venue']).count().sort_values('Neighborhood',ascending=False).iloc[:5,0])
CL7.rename(columns={'Borough':'Count'})

	Borough	Neighborhood	Latitude	Longitude
0	Bronx	Wakefield	40.894705	-73.847201
1	Bronx	Co-op City	40.874294	-73.829939
2	Bronx	Eastchester	40.887556	-73.827806
3	Bronx	Fieldston	40.895437	-73.905643
4	Bronx	Riverdale	40.890834	-73.912585

	name	categories	id	lat	lng
0	Lollipops Gelato	Dessert Shop	4c537892fd2ea593cb077a28	40.894123	-73.845892
1	Rite Aid	Pharmacy	4d6af9426107f04dedeb297a	40.896649	-73.844846
2	Carvel Ice Cream	Ice Cream Shop	4c783cef3badb1f7e4244b54	40.890487	-73.848568
3	Walgreens	Pharmacy	5d5f5044d0ae1c0008f043c3	40.896687	-73.844850
4	Dunkin'	Donut Shop	4c25c212f1272d7f836385c5	40.890459	-73.849089
5	Shell	Gas Station	4c81a91c51ada1cd87741510	40.894187	-73.845862
6	Cooler Runnings Jamaican Restaurant Inc	Caribbean Restaurant	508af256e4b0578944c87392	40.898083	-73.850259
7	SUBWAY	Sandwich Place	4d33665fb6093704b80001e0	40.890468	-73.849152
8	Central Deli	Deli / Bodega	4f32458019836c91c7c734ff	40.896728	-73.844387
9	Louis Pizza	Pizza Place	55aa92ac498e24734cd2e378	40.898399	-73.848810
10	Koss Quick Wash	Laundromat	5681717c498e9b9cf4d8c187	40.891281	-73.849904

	Neighborhood	Neighborhood Latitude	Neighborhood Longitude	Venue	Venue id	Venue Latitude	Venue Longitude	Venue Category
0	Wakefield	40.894705	-73.847201	Lollipops Gelato	4c537892fd2ea593cb077a28	40.894123	-73.845892	Dessert Shop
1	Wakefield	40.894705	-73.847201	Rite Aid	4d6af9426107f04dedeb297a	40.896649	-73.844846	Pharmacy
2	Wakefield	40.894705	-73.847201	Carvel Ice Cream	4c783cef3badb1f7e4244b54	40.890487	-73.848568	Ice Cream Shop
3	Wakefield	40.894705	-73.847201	Walgreens	5d5f5044d0ae1c0008f043c3	40.896687	-73.844850	Pharmacy
4	Wakefield	40.894705	-73.847201	Dunkin'	4c25c212f1272d7f836385c5	40.890459	-73.849089	Donut Shop

	Neighborhood	Venue
0	Murray Hill	147
1	Chelsea	105
2	Lenox Hill	100
3	Little Italy	100
4	Chinatown	100
5	Civic Center	100
6	Clinton	100
7	Downtown	100
8	East Village	100
9	Financial District	100

	Neighborhood	Dessert Shop	Donut Shop	Ice Cream Shop	Pharmacy
0	Wakefield	1	0	0	0
1	Wakefield	0	0	0	1
2	Wakefield	0	0	1	0
3	Wakefield	0	0	0	1
4	Wakefield	0	1	0	0

	Neighborhood	Athletics & Sports	Bagel Shop	Bakery	Boat or Ferry	Breakfast Spot	Bus Station	Bus Stop	Check Cashing Service	Chinese Restaurant	Coffee Shop	Cosmetics Shop	Deli / Bodega	Department Store	Dessert Shop	Diner	Discount Store	Donut Shop	Electronics Store	Fast Food Restaurant	Food Truck	Fried Chicken Joint	Gas Station	Grocery Store	Home Service	Hotel	Intersection	Italian Restaurant	Martial Arts Dojo	Mediterranean Restaurant	Mexican Restaurant	Middle Eastern Restaurant	Outdoors & Recreation	Park	Pharmacy	Pizza Place	Playground	Polish Restaurant	Pub	Restaurant	Sandwich Place	Spa	Spanish Restaurant	Sports Bar	Supermarket	Train Station
0	Allerton	0.00	0.0	0.030303	0.000000	0.030303	0.060606	0.000000	0.030303	0.060606	0.00	0.030303	0.090909	0.030303	0.030303	0.000	0.030303	0.030303	0.030303	0.030303	0.00	0.030303	0.030303	0.030303	0.00	0.00	0.030303	0.0	0.030303	0.00	0.030303	0.00	0.00	0.000	0.030303	0.121212	0.030303	0.00	0.000	0.000	0.00	0.030303	0.030303	0.000	0.060606	0.000
1	Annadale	0.00	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.000000	0.125	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.00	0.00	0.000000	0.0	0.000000	0.00	0.000000	0.00	0.00	0.125	0.000000	0.250000	0.000000	0.00	0.125	0.125	0.00	0.000000	0.000000	0.125	0.000000	0.125
2	Arden Heights	0.00	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.25	0.000000	0.000000	0.000000	0.000000	0.000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.000000	0.25	0.00	0.000000	0.0	0.000000	0.00	0.000000	0.00	0.00	0.000	0.250000	0.250000	0.000000	0.00	0.000	0.000	0.00	0.000000	0.000000	0.000	0.000000	0.000
3	Arlington	0.00	0.0	0.000000	0.166667	0.000000	0.000000	0.333333	0.000000	0.000000	0.00	0.000000	0.166667	0.000000	0.000000	0.000	0.000000	0.000000	0.000000	0.000000	0.00	0.000000	0.000000	0.166667	0.00	0.00	0.166667	0.0	0.000000	0.00	0.000000	0.00	0.00	0.000	0.000000	0.000000	0.000000	0.00	0.000	0.000	0.00	0.000000	0.000000	0.000	0.000000	0.000
4	Arrochar	0.05	0.1	0.000000	0.000000	0.000000	0.000000	0.150000	0.000000	0.000000	0.00	0.050000	0.100000	0.000000	0.000000	0.000	0.000000	0.000000	0.000000	0.000000	0.05	0.000000	0.000000	0.000000	0.00	0.05	0.000000	0.1	0.000000	0.05	0.000000	0.05	0.05	0.000	0.000000	0.050000	0.000000	0.05	0.000	0.000	0.05	0.000000	0.000000	0.000	0.050000	0.000

	Count
1st Most Common Venue
Pizza Place	35
Chinese Restaurant	10
Pharmacy	8
Caribbean Restaurant	7
Donut Shop	7